What is the difference between on-policy and off-policy learning?

Theoretical Background:

In reinforcement learning (RL), the goal is to train an agent to take actions in an environment to maximize some notion of cumulative reward. The distinction between on-policy and off-policy methods is crucial in determining how the agent learns and updates its policy.

• On-Policy Learning: In on-policy learning, the agent learns the value of the policy it is currently following. This means that the policy used for taking actions is the same as the policy being improved. An example of this is the SARSA algorithm, which updates its Q-values based on the action taken by the current policy.

• Off-Policy Learning: In off-policy learning, the agent learns the value of the optimal policy independently of the agent's actions. This allows the agent to learn from older experiences generated by different policies. Q-Learning is a classic example of this, where the policy being improved is not necessarily the one used to generate the data.

Practical Applications:

• On-Policy: Often used in environments where the policy needs to be improved gradually and safely, such as in robotics where abrupt changes in policy may cause harm.

• Off-Policy: Commonly used in applications where the agent can learn from a broader set of experiences, such as in games where exploration can be simulated extensively, or in environments where data collection is expensive and off-policy learning allows for better utilization of available data.

Code Examples:

While a full code example is beyond this explanation, consider how SARSA and Q-Learning update their Q-values. SARSA uses the policy to select the next action and updates based on this, while Q-Learning updates based on the maximum action value.

Diagrams:

Here's a simple diagram to illustrate the difference:

graph TD;
  A[Start State] --> B{On-Policy Action}
  A --> C{Off-Policy Action}
  B --> D[Policy Update using On-Policy]
  C --> E[Policy Update using Off-Policy]

External References:

• Reinforcement Learning: An Introduction by Sutton and Barto provides a comprehensive overview of these methods.
• OpenAI Spinning Up offers practical implementations and explanations of various RL algorithms, including on-policy and off-policy methods.

These resources and insights can provide further understanding of the fundamental differences between on-policy and off-policy learning in reinforcement learning.